
import pandas as pd
import numpy as np


# Display all columns
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)


from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from matplotlib.patches import FancyBboxPatch
import matplotlib.transforms as transforms
import matplotlib.gridspec as gridspec
import matplotlib.ticker as mtick
import matplotlib.pyplot as plt
from matplotlib import style, offsetbox
import seaborn as sns
import matplotlib

style.use('ggplot')

%matplotlib inline
#sns.set_palette("dark")
#style.use('ggplot')

#sns.set_style('darkgrid')
matplotlib.rcParams['font.size'] = 15
matplotlib.rcParams['font.weight'] = 'bold'
#matplotlib.rcParams['figure.figsize'] = (50, 20)
#matplotlib.rcParams['figure.facecolor'] = '#00000000'


nugget_regular_season_df = pd.read_csv('2024-2025 Denver Nuggets Players Stats.csv')


nugget_regular_season_df


nugget_regular_season_df.groupby(['PLAYER'])['Pts'].mean()

PLAYER
Aaron Gordon         13.705882
Christian Braun      13.857143
Dario Saric           3.416667
DeAndre Jordan        3.333333
Hunter Tyson          2.269231
Jalen Pickett         3.090909
Jamal Murray         19.433333
Julian Strawther      9.472222
Michael Porter       19.027778
Nikola Jokic         31.516129
P.J. Hall             0.666667
Peyton Watson         8.805556
Russell Westbrook    12.611111
Spencer Jones         0.400000
Trey Alexander        0.750000
Vlatko Cancar         2.250000
Zeke Nnaji            1.190476
Name: Pts, dtype: float64


#nugget_regular_season_df = nugget_regular_season_df[nugget_regular_season_df.PLAYER == player ].tail().reset_index(drop=True)
nugget_regular_season_df["vs_opp_win_loss"] =  nugget_regular_season_df['W/L']+' '+nugget_regular_season_df["Hm/Aw"]+' '+ nugget_regular_season_df["Opp"] 
nugget_regular_season_df["GM_DAY"] =  nugget_regular_season_df["Date"]+' '+nugget_regular_season_df["vs_opp_win_loss"]


def perplayer_performance():
    
    
    players_df = nugget_regular_season_df.groupby(["PLAYER"])[['MIN','Pts','REB','AST', 'BLK',
                                                    'TOV', 'FGA', 'FGM',
                                                    '3PA', '3PM','2PM','2PA',"OREB","DREB", 
                                                    'FTA', 'FTM', 'PF','+/-','EFF']].sum().astype(int).sort_values(by='Pts',ascending=False)
    
    style.use('ggplot')
    plt.figure(figsize=(30, 18))
    plt.ylabel("Players", fontsize=15, color='black', fontweight='bold')
    plt.tick_params(axis='both', which='major', labelcolor="black", labelsize=18, labelbottom=True, 
                    bottom=False, top=False, labeltop=True)

    sns.heatmap(players_df, annot=True, fmt="d", cmap='viridis_r', annot_kws={"size":22}).set_title(f"Players Performance Data Scorecard Overview ", fontdict={'size': 30}, pad=30, color='#000')

perplayer_performance()


nugget_regular_season_df['Pts'].sum()

4338


def perplayer_performance():
    
    
    players_df = nugget_regular_season_df.groupby(["PLAYER"])[['MIN','Pts','REB','AST', 'BLK',
                                                    'TOV', 'FGA', 'FGM',
                                                    '3PA', '3PM','2PM','2PA',"OREB","DREB", 
                                                    'FTA', 'FTM', 'PF','+/-','EFF']].mean().astype(int).sort_values(by='Pts',ascending=False)
    
    style.use('ggplot')
    plt.figure(figsize=(30, 18))
    plt.ylabel("Players", fontsize=15, color='black', fontweight='bold')
    plt.tick_params(axis='both', which='major', labelcolor="black", labelsize=18, labelbottom=True, 
                    bottom=False, top=False, labeltop=True)

    sns.heatmap(players_df, annot=True, fmt="d", cmap='viridis_r', annot_kws={"size":22}).set_title(f" Players Average Performaces Overview", fontdict={'size': 30}, pad=30, color='#000')

perplayer_performance()


def performance_trend(players_game, player,cmaps):
    players_df = players_game[players_game['PLAYER'].isin([player])]
    
    players_df = players_df.groupby(["Date", "W/L", "Hm/Aw","Opp"])[['MIN','Pts','REB','AST', 'BLK',
                                                    'TOV', 'FGA', 'FGM',
                                                    '3PA', '3PM','2PM','2PA',"OREB","DREB", 
                                                    'FTA', 'FTM', 'PF','+/-','EFF']].sum()#.astype(int)
    
    style.use('ggplot')
    plt.figure(figsize=(18, 15))
    plt.ylabel("Players", fontsize=15, color='black', fontweight='bold')
    plt.tick_params(axis='both', which='major', labelcolor="black", labelsize=13, labelbottom=True, 
                    bottom=False, top=False, labeltop=True)

    sns.heatmap(players_df, annot=True, fmt="d", cmap=cmaps, annot_kws={"size":15}).set_title(f"Performance Data Scorecard for {player}", fontdict={'size': 16}, pad=15, color='#000')


def statistical_summary(stat, player, player_name, cmap):
    players_df = (nugget_regular_season_df[nugget_regular_season_df[stat].isin([player])]).describe()
    players_df.drop(columns=['MIN' ], inplace=True)
    
    # Removed the count row and changing the floating numbers to type int.
    stat_heatmap = (players_df.loc[[ 'mean', 'std', 'min', '25%', '50%', '75%', 'max']]).astype(int)
    
    style.use('ggplot')
    plt.figure(figsize=(15, 5))
    plt.tick_params(axis='both', which='major',labelcolor='black', labelsize=11,labelbottom=True, bottom=False, top=False, labeltop=True)
    
    
    sns.heatmap(stat_heatmap, annot=True,fmt="d",  cmap=cmap, annot_kws={"size": 14}).set_title(f"Statistical Summary For {player_name}", fontdict={'size': 16},pad=15, color='#000' );


def season_average(player, image_path, annotation_text, color, mycmap):   
    nugget_group_df = nugget_regular_season_df.groupby(["PLAYER"])[['Pts', 'FGA', 'FGM', '3PM', '3PA', 'FTM', 'FTA', 'OREB', 'DREB', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF']].mean().reset_index()
    player_mean_stats = nugget_group_df[nugget_group_df['PLAYER'] == player]
    
    # Collect the relevant statistics
    player_mean_stats = player_mean_stats[['TOV', 'STL', 'BLK', 'FTM', '3PM', 'FGM', 'AST', 'REB', 'Pts']]
    
    # Convert the stats to numeric values (in case of any non-numeric values)
    mean_stats = player_mean_stats.apply(pd.to_numeric, errors='coerce').squeeze()
    
    my_cmap = mycmap#plt.get_cmap("Accent")
    style.use('dark_background')
    plt.rcParams.update({
        'figure.facecolor': 'black',
        'font.size': 10,
    })
    
    fig, ax = plt.subplots(figsize=(20, 10))
    
    # Add the player's picture
    pic = plt.imread(image_path)
    imagebox = offsetbox.OffsetImage(pic, zoom=0.1)
    ab = offsetbox.AnnotationBbox(imagebox, (0.7, 0.54), xycoords='axes fraction', frameon=False)
    ax.add_artist(ab)
    
    # Add annotation text with transparent background and adjusted box dimensions
    plt.text(
        0.42, 0.33, annotation_text, fontsize=16, color=color, wrap=True, 
        ha='left', va='center', transform=ax.transAxes,
        bbox=dict(facecolor="none", edgecolor="orange", boxstyle="round,pad=1.3")
    )

    # Apply normalization to the numeric values
    norm = plt.Normalize(mean_stats.min(), mean_stats.max())
    colors = my_cmap(norm(mean_stats))
    
    # Create the horizontal bar chart without borderlines
    bars = ax.barh(mean_stats.index, mean_stats.values, color=colors, edgecolor='none')
    
    # Add value labels above each bar
    for bar in bars:
        width = bar.get_width()
        plt.text(width, bar.get_y() + bar.get_height() / 2, f"{width:.1f}", ha='left', va='center', color="white", fontweight='bold', fontsize=20)
    
    # Remove the spines
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    
    plt.yticks(fontweight='bold', fontsize=20, color="yellowgreen")
    plt.xticks(fontweight='bold', fontsize=20, color="yellowgreen")
    plt.title(f"{player} - Current Season's Average", fontsize=30, fontweight='bold', pad=20)
    
    plt.grid(False)
    plt.show()


def players_total_stats(stat, player):
    players_stats = nugget_regular_season_df[nugget_regular_season_df[stat].isin([player])]
    total_stat = players_stats[['Pts','FGM','FGA','3PM','3PA','2PM', '2PA', 'FTM', 'FTA','OREB','DREB', 'AST', 'STL', 'BLK', 'TOV', 'EFF']].sum().astype(int)
    total_stats = total_stat.reset_index()
    total_stats.columns = ['statistics', 'values']
    return total_stats


def overall_stats(player, limit ):   
    # Define the fg_percent function to return data
    def fg_percent(fg_made, fg_attempted):
        made_percentage = (fg_made / fg_attempted) * 100
        missed_percentage = 100 - made_percentage
        labels = ['made', 'missed']
        sizes = [made_percentage, missed_percentage]
        colors = ['#15B01A', 'red']
        return {'sizes': sizes, 'labels': labels, 'colors': colors}

    # Creating a figure
    fig = plt.figure(figsize=(20, 10),facecolor='cyan')
    style.use('dark_background')
    # Set the spacing between subplots
    plt.subplots_adjust(hspace=25)

    # Defining the grid layout
    gs = gridspec.GridSpec(2, 4, width_ratios=[1, 1, 1,1])

    # Creating subplots
    ax1 = fig.add_subplot(gs[0, :])
    ax2 = fig.add_subplot(gs[1, 0])
    ax3 = fig.add_subplot(gs[1, 1])
    ax4 = fig.add_subplot(gs[1, 2])
    ax5 = fig.add_subplot(gs[1, 3])
 



    #######################################################################################
    ######################################################################################
    my_cmap = plt.get_cmap("coolwarm")
    #my_cmap = plt.get_cmap("twilight_shifted_r")
    style.use('dark_background')

    norm = plt.Normalize(total_stats['values'].min(),total_stats['values'].max())
    colors = my_cmap(norm(total_stats['values']))


    ax1.bar(total_stats['statistics'], total_stats['values'], color=colors, edgecolor='black')
    # Adding value labels on top of each bar
    for i, value in enumerate(total_stats['values']):
        ax1.text(i, value, str(value), ha='center', va='bottom', fontsize=20, color='orange')

    # Customize the plot
    ax1.set_xlabel('Statistics',fontsize=30, fontweight='bold', color='black')
    ax1.set_ylabel('Totals',fontsize=30, fontweight='bold', color='black')
    ax1.set_title(f"Regular Season Overall Sum of Box Scores For {player}.",fontsize=30,fontweight='bold', color='black')
    ax1.tick_params(axis='x', colors='black',labelsize=20);

    ax1.set_ylim(0, limit)
    ax1.grid(False)
    ###################################################

    #######################################################################################
    ##########################################################################################
    cmap = plt.get_cmap("Reds")  
    colors = cmap([0.1, 0.5, 0.9]) 
    # Plot the pie chart for ax1
    data1 = fg_percent(total_stats.iloc[1, 1], total_stats.iloc[2, 1])
    ax2.pie(data1['sizes'], labels=data1['labels'],
            colors=colors,
            wedgeprops=dict(width=0.3),
            autopct='%1.1f%%', 
            textprops={'size': 'xx-large', 'color': 'black'},startangle=126)
    ax2.set_title(' Total Field Goals%',fontweight='bold',fontsize=20,color='black')
    ax2.axis('equal')
    #######################################################################################
    # Plot the pie chart for ax2
    data2 = fg_percent(total_stats.iloc[3, 1], total_stats.iloc[4, 1])
    ax3.pie(data2['sizes'], labels=data2['labels'],
            colors=data2['colors'],
            wedgeprops=dict(width=0.3),
            autopct='%1.1f%%', 
            textprops={'size': 'xx-large', 'color': 'black'},startangle=270)
    ax3.set_title(' Total 3 points%',fontweight='bold',fontsize=20,color='black')
    ax3.axis('equal')

    #######################################################################################
    # Plot the pie chart for ax3
    data3 = fg_percent(total_stats.iloc[5, 1], total_stats.iloc[6, 1])
    ax4.pie(data3['sizes'], labels=data3['labels'],
            colors=data3['colors'],
            wedgeprops=dict(width=0.3),
            autopct='%1.1f%%', 
            textprops={'size': 'xx-large', 'color': 'black'},startangle=15)
    ax4.set_title('Total 2 Points%',fontweight='bold',fontsize=20,color='black')
    ax4.axis('equal')


    #######################################################################################
    
    # Plot the pie chart for ax3
    data4 = fg_percent(total_stats.iloc[7, 1], total_stats.iloc[8, 1])
    ax5.pie(data4['sizes'], labels=data4['labels'],
            colors=data4['colors'],
            wedgeprops=dict(width=0.3),
            autopct='%1.1f%%', 
            textprops={'size': 'xx-large', 'color': 'black'},startangle=15)
    ax5.set_title('Total Free-Throws%',fontweight='bold',fontsize=20,color='black')
    ax5.axis('equal')


    #######################################################################################
  



    fig.tight_layout(pad=3)

    # Show the plot
    plt.show()


def tripple_double_double(player):
    # Initialize the counter and list to store triple-double performances
    triple_double_count = 0
    double_double_count = 0
    triple_double_list = []
    
    triple_double_counts = nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin([player])]
    
    # Check for triple-double performances and count them
    for index, row in triple_double_counts.iterrows():
        pts, reb, ast, opp, date = row['Pts'], row['REB'], row['AST'], row['Opp'], row['GM_DAY']
        if pts >= 10 and reb >= 10 and ast >= 10:
            #print(f"Triple-double performance: {pts, reb, ast} against {opp} on {date}")
            triple_double_count += 1
            triple_double_list.append((date, opp, pts, reb, ast))
        elif (pts >= 10 and reb >= 10 and ast < 10) or (pts >= 10 and reb < 10 and ast >= 10) or (pts < 10 and reb >= 10 and ast >= 10):
            #print(f"Double-double performance: {pts, reb, ast} against {opp} on {date}")
            double_double_count += 1
    
    # Create a DataFrame to store the triple-double performances
    triple_double_df = pd.DataFrame(triple_double_list, columns=['Date', 'Opp', 'Pts', 'Rebs', 'Ast'])
    
    # Print the total count of triple-double and double-double performances
    print(f"Total triple-double performances for {player}: {triple_double_count}")
    print(f"Total double-double performances for {player}: {double_double_count}")
    
    return triple_double_df



#tripple_double_double("Nikola Jokic")


def triple_double_overview(player):
    triple_double_count = triple_double_df.groupby(["Date",])[['Pts','Rebs','Ast']].sum().astype(int)
    style.use('dark_background')

    
    plt.figure(figsize=(3, 5))
    plt.tick_params(axis='both', which='major', labelsize=11,labelcolor='white',labelbottom=True, bottom=False, top=False, labeltop=True)


    sns.heatmap(triple_double_count, annot=True,fmt="d",  cmap='viridis_r',annot_kws={"size": 14}, color='#000' , cbar=False).set_title(f" {player} 2024/2025 Triple Double Overview.", fontdict={'size': 16},pad=15, color='white' );


def home_away_performances(player):

    #Displaying the plots in a subplots
    def subplots():
            # Create a figure
            fig = plt.figure(figsize=(20,10));
            fig.set_facecolor("orange")


            plt.rcParams.update({'font.size': 10, })

            # Defining the grid layout with different width ratios for each row

            gs = gridspec.GridSpec(2, 2)


            # Create subplots
            ax1 = fig.add_subplot(gs[0, :]);
            ax2 = fig.add_subplot(gs[1, :]);
            
            style.use('ggplot')

            %matplotlib inline
            #sns.set_palette("dark")
            #style.use('ggplot')

            #sns.set_style('darkgrid')


    ######################################################################################################
    ####################### FREQUENCY DISTRIBUTION OF PERFORMANCES IN HOME GAMES #########################
    ######################################################################################################
            player_home_performance = nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin([player])]
            home_games = player_home_performance[player_home_performance['Hm/Aw'] == 'vs' ]
            home_game = home_games.describe()

            home_game = (home_game.loc[[ 'max', 'min']]).astype(int)




            # Accessing the indexes and values of the away game data
            stats = home_game.loc['max'].index
            home_max_values = home_game.loc['max'].values
            home_min_values = home_game.loc['min'].values
            y = np.arange(len(stats))  # The label locations
            width = 0.4  # The width of the bars

            # Plot the bars for each attribute
            bar3 = ax1.bar(y - width, home_max_values, width, label='Home max values', color='green')
            bar4 = ax1.bar(y, home_min_values, width, label='Home min values')

            # Add value labels on top of each bar3
            for p in bar3:
                height = p.get_height()

                ax1.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+3),
                            ha='center', va='center', fontsize=15, color='black', fontweight='bold')

            # Add value labels on top of each bar4
            for p in bar4:
                height = p.get_height()

                ax1.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+3),
                            ha='center', va='center', fontsize=15, color='black', fontweight='bold')

            # Add labels, title, and custom x-axis tick labels
            #ax2.set_ylabel('Counts',fontsize=15,fontweight='bold')
            ax1.set_title('Performance Comparison: Home Games', fontsize=20, fontweight='bold',color='black')
            ax1.set_xticks(y-width/2)
            ax1.tick_params(axis='x', labelsize=15, labelcolor='black')  # Adjust the font size as needed
            ax1.tick_params(axis='y', labelsize=15, labelcolor='black')
            ax1.set_xticklabels(stats)
            ax1.legend(loc='upper center', ncols=3,fontsize=15,labelcolor='black')
            ax1.set_ylim(0, 70)

    ######################################################################################################
    ####################### FREQUENCY DISTRIBUTION OF PERFORMANCES IN AWAY GAMES #########################
    ######################################################################################################
            player_home_performance = nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin([player])]                                                                                           
            away_games = player_home_performance[player_home_performance['Hm/Aw'] == '@' ]
            away_game = away_games.describe()

            away_game = (away_game.loc[[ 'max', 'min']]).astype(int)



            # Accessing the indexes and values of the away game data
            stats =away_game.loc['max'].index
            away_max_values = away_game.loc['max'].values
            away_min_values = away_game.loc['min'].values
            y = np.arange(len(stats))  # The label locations
            width = 0.4  # The width of the bars

            # Plot the bars for each attribute
            bar3 = ax2.bar(y - width, away_max_values, width, label='Away max values', color='green')
            bar4 = ax2.bar(y, away_min_values, width, label='Away min values')

            # Add value labels on top of each bar3
            for p in bar3:
                height = p.get_height()

                ax2.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+3),
                            ha='center', va='center', fontsize=15, color='black', fontweight='bold')

            # Add value labels on top of each bar4
            for p in bar4:
                height = p.get_height()

                ax2.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+3),
                            ha='center', va='center', fontsize=15, color='black', fontweight='bold')

            # Add labels, title, and custom x-axis tick labels
            #ax2.set_ylabel('Counts',fontsize=15,fontweight='bold')
            ax2.set_title('Performance Comparison: Away Games', fontsize=20, fontweight='bold', color='black')
            ax2.set_xticks(y-width/2)
            ax2.tick_params(axis='x', labelsize=15, labelcolor='black')  # Adjust the font size as needed
            ax2.tick_params(axis='y', labelsize=15, labelcolor='black')
            ax2.set_xticklabels(stats)
            ax2.legend(loc='upper center', ncols=3,fontsize=15, labelcolor='black')
            ax2.set_ylim(0, 70)
            plt.tight_layout(pad=1);
            
            
            

    subplots()        
    #'#800000'


def fg_3pts_ft(player): 
    # Defining the fg_percent function to return data
    def fg_percent(fg_made, fg_attempted):
        if fg_attempted == 0:
            return {'sizes': [0, 100], 'labels': ['made', 'missed'], 'colors': ['#15B01A', 'red']}
        made_percentage = (fg_made / fg_attempted) * 100
        missed_percentage = 100 - made_percentage
        labels = ['made', 'missed']
        sizes = [made_percentage, missed_percentage]
        colors = ['#15B01A', 'red']
        return {'sizes': sizes, 'labels': labels, 'colors': colors}

    # Create a figure
    fig = plt.figure(figsize=(20, 18),facecolor='orange')
    style.use('ggplot')

    # Set the spacing between subplots
    plt.subplots_adjust(hspace=20) 

    # Define the grid layout
    gs = gridspec.GridSpec(6, 5, width_ratios=[1, 1, 1, 1, 1])

    # Create subplots
    ax1 = fig.add_subplot(gs[0,:])
    ax2 = fig.add_subplot(gs[1, 0])
    ax3 = fig.add_subplot(gs[1, 1])
    ax4 = fig.add_subplot(gs[1, 2])
    ax5 = fig.add_subplot(gs[1, 3])
    ax6 = fig.add_subplot(gs[1, 4])

    ax7 = fig.add_subplot(gs[2,:])  # Adjust the width for ax1
    ax8 = fig.add_subplot(gs[3, 0])
    ax9 = fig.add_subplot(gs[3, 1])
    ax10 = fig.add_subplot(gs[3, 2])
    ax11 = fig.add_subplot(gs[3, 3])
    ax12 = fig.add_subplot(gs[3, 4])

    ax13 = fig.add_subplot(gs[4,:])  # Adjust the width for ax1
    ax14 = fig.add_subplot(gs[5, 0])
    ax15 = fig.add_subplot(gs[5, 1])
    ax16 = fig.add_subplot(gs[5, 2])
    ax17 = fig.add_subplot(gs[5, 3])
    ax18 = fig.add_subplot(gs[5, 4])


    #######################################################################################
    ##########################    FIELD GOALS         #####################################
    #######################################################################################
    
    
    last_5_games = nugget_regular_season_df[nugget_regular_season_df.PLAYER == player ].tail().reset_index(drop=True)
    last_5_games["vs_opp_win_loss"] = last_5_games["Hm/Aw"]+' '+last_5_games["Opp"]+' '+last_5_games['W/L'] 
    last_5_games["GM_DAY"] = last_5_games["Date"]+' '+last_5_games["vs_opp_win_loss"]  

    
    # Melting  the DataFrame to create a clean format
    last_5_games_melted = pd.melt(last_5_games , id_vars='GM_DAY', value_vars=['FGA', 'FGM'])
    colors= ['red', 'green']
    sns.barplot(data=last_5_games_melted, x='GM_DAY', y='value', hue="variable", palette=colors, ax=ax1)

    # Add value labels on top of each bar
    for p in ax1.patches:
        height = p.get_height()

        ax1.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+2),
                    ha='center', va='center', fontsize=20, color='black', fontweight='bold')

    # Customize the plot
    ax1.set_ylim(0, 50)  # Use set_ylim instead of ylim
    ax1.set_title(f'{player} Last 5 Games Field Goals (Attempts and Made)',  fontweight='bold', color='black',fontsize=25,)
    ax1.set_xlabel('',fontsize=15, fontweight='bold', color='black')
    # Set the x-axis tick label color to purple
    ax1.tick_params(axis='x', colors='black', labelsize=15)
    #######################################################################################
    # Plot the pie chart for ax2
    data1 = fg_percent(last_5_games.iloc[0, 7], last_5_games.iloc[0, 8])
    ax2.pie(data1['sizes'], labels=data1['labels'],
            colors=data1['colors'],
            wedgeprops=dict(width=0.3),
            autopct='%1.1f%%', 
            textprops={'size': 'medium', 'color': 'black'},startangle=126)
    ax2.set_title('',fontweight='bold',fontsize=17,color='black')
    ax2.axis('equal')
    ########################################################################################

    # Plot the pie chart for ax3
    data2 = fg_percent(last_5_games.iloc[1, 7], last_5_games.iloc[1, 8])
    ax3.pie(data2['sizes'], labels=data2['labels'], 
            colors=data2['colors'], 
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%',  
            textprops={'size': 'medium', 'color': 'black'},startangle=270)
    ax3.set_title('',fontweight='bold',fontsize=17,color='black')
    ax3.axis('equal')
    #######################################################################################
    # Plot the pie chart for ax4
    data3 = fg_percent(last_5_games.iloc[2, 7], last_5_games.iloc[2, 8])
    ax4.pie(data3['sizes'], labels=data3['labels'],
            colors=data3['colors'],
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%', startangle=40,
            textprops={'size': 'medium', 'color': 'black'})
    ax4.set_title('Field Goals %',fontweight='bold',fontsize=17,color='black')
    ax4.axis('equal')
    #######################################################################################
    # Plot the pie chart for ax5
    data4 = fg_percent(last_5_games.iloc[3, 7], last_5_games.iloc[3, 8])
    ax5.pie(data4['sizes'], labels=data4['labels'], 
            colors=data4['colors'], 
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%', startangle=10,
            textprops={'size': 'medium', 'color': 'black'})
    ax5.set_title('',fontweight='bold',fontsize=17,color='black')
    ax5.axis('equal')
    #######################################################################################
    # Plot the pie chart for ax6
    data5 = fg_percent(last_5_games["FGM"][4], last_5_games["FGA"][4])
    ax6.pie(data5['sizes'], labels=data5['labels'], 
            colors=data5['colors'],
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%',
            textprops={'size': 'medium', 'color': 'black'},
            startangle=60)
    ax6.set_title('',fontweight='bold',fontsize=17,color='black')
    ax6.axis('equal')

    #######################################################################################
    ##########################       3 POINTS         #####################################
    #######################################################################################

    # Melting  the DataFrame to create a clean format
    last_5_games_melted = pd.melt(last_5_games , id_vars='GM_DAY', value_vars=['3PA', '3PM'])
    colors= ['orange', 'green']
    sns.barplot(data=last_5_games_melted, x='GM_DAY', y='value', hue="variable", palette=colors, ax=ax7)

    # Add value labels on top of each bar
    for p in ax7.patches:
        height = p.get_height()

        ax7.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+1),
                    ha='center', va='center', fontsize=20, color='black', fontweight='bold')

    # Customize the plot
    ax7.set_ylim(0, 20)  # Use set_ylim instead of ylim
    ax7.set_title(f'3 Points (Attempts and Made)',  fontweight='bold', color='black',fontsize=25,)
    ax7.set_xlabel('',fontsize=15, fontweight='bold', color='black',)
    # Set the x-axis tick label color to purple
    ax7.tick_params(axis='x', colors='black',labelsize=15)
    #######################################################################################
    # Plot the pie chart for ax8
    data6 = fg_percent(last_5_games.at[0, '3PM'], last_5_games.at[0, '3PA'])
    ax8.pie(data6['sizes'], labels=data6['labels'],
            colors=data6['colors'],
            wedgeprops=dict(width=0.3),
            autopct='%1.1f%%', 
            textprops={'size': 'medium', 'color': 'black'},startangle=126)
    ax8.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
    ax8.axis('equal')
    ########################################################################################

    # Plot the pie chart for ax3
    data7 = fg_percent(last_5_games.at[1, '3PM'], last_5_games.at[1, '3PA'])
    ax9.pie(data7['sizes'], labels=data7['labels'], 
            colors=data7['colors'], 
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%',  
            textprops={'size': 'medium', 'color': 'black'},startangle=270)
    ax9.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
    ax9.axis('equal')
    #######################################################################################
    # Plot the pie chart for ax4
    data8 = fg_percent(last_5_games.at[2, '3PM'], last_5_games.at[2, '3PA'])
    ax10.pie(data8['sizes'], labels=data8['labels'],
            colors=data8['colors'],
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%', startangle=40,
            textprops={'size': 'medium', 'color': 'black'})
    ax10.set_title('3 Points %',fontweight='bold',fontsize=17,color='black',pad=5)
    ax10.axis('equal')
    #######################################################################################

    # Plot the pie chart for ax5
    data9 = fg_percent(last_5_games.at[3, '3PM'], last_5_games.at[3, '3PA'])
    ax11.pie(data9['sizes'], labels=data9['labels'], 
            colors=data4['colors'], 
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%', startangle=10,
            textprops={'size': 'medium', 'color': 'black'})
    ax11.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
    ax11.axis('equal')
    #######################################################################################
    # Plot the pie chart for ax6
    data10 = fg_percent(last_5_games.at[4, '3PM'], last_5_games.at[4, '3PA'])
    ax12.pie(data10['sizes'], labels=data10['labels'], 
            colors=data5['colors'],
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%',
            textprops={'size': 'medium', 'color': 'black'},
            startangle=60)
    ax12.set_title('',fontsize=17,fontweight='bold',color='black',pad=5)
    ax12.axis('equal')

    #######################################################################################
    ##########################    FREE THROW        #####################################
    #######################################################################################

    # Melting  the DataFrame to create a clean format
    last_5_games_melted = pd.melt(last_5_games , id_vars='GM_DAY', value_vars=['FTA', 'FTM'])
    colors= ['red', '#00FF00']
    sns.barplot(data=last_5_games_melted, x='GM_DAY', y='value', hue="variable", palette=colors, ax=ax13)

    # Add value labels on top of each bar
    for p in ax13.patches:
        height = p.get_height()

        ax13.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+1),
                    ha='center', va='center', fontsize=20, color='black', fontweight='bold')

    # Customize the plot
    ax13.set_ylim(0, 20)  # Use set_ylim instead of ylim
    ax13.set_title('Freethrows(Attempts and Made)',  fontweight='bold', color='black',fontsize=25,)
    ax13.set_xlabel('',fontsize=15, fontweight='bold', color='black',)
    # Set the x-axis tick label color to purple
    ax13.tick_params(axis='x', colors='black',labelsize=15)
    #######################################################################################
    # Plot the pie chart for ax8
    data11 = fg_percent(last_5_games.at[0, 'FTM'], last_5_games.at[0, 'FTA'])
    ax14.pie(data11['sizes'], labels=data11['labels'],
            colors=data11['colors'],
            wedgeprops=dict(width=0.3),
            autopct='%1.1f%%', 
            textprops={'size': 'medium', 'color': 'black'},startangle=126)
    ax14.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
    ax14.axis('equal')
    ########################################################################################

    # Plot the pie chart for ax3
    data12 = fg_percent(last_5_games.at[1, 'FTM'], last_5_games.at[1, 'FTA'])
    ax15.pie(data12['sizes'], labels=data12['labels'], 
            colors=data12['colors'], 
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%',  
            textprops={'size': 'medium', 'color': 'black'},startangle=270)
    ax15.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
    ax15.axis('equal')
    #######################################################################################
    # Plot the pie chart for ax4
    data13 = fg_percent(last_5_games.at[2, 'FTM'], last_5_games.at[2, 'FTA'])
    ax16.pie(data13['sizes'], labels=data13['labels'],
            colors=data13['colors'],
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%', startangle=40,
            textprops={'size': 'medium', 'color': 'black'})
    ax16.set_title('Freethrows %',fontweight='bold',fontsize=17,color='black',pad=5)
    ax16.axis('equal')
    #######################################################################################

    # Plot the pie chart for ax5
    data14 = fg_percent(last_5_games.at[3, 'FTM'], last_5_games.at[3, 'FTA'])
    ax17.pie(data14['sizes'], labels=data14['labels'], 
            colors=data14['colors'], 
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%', startangle=10,
            textprops={'size': 'medium', 'color': 'black'})
    ax17.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
    ax17.axis('equal')
    #######################################################################################
    # Plot the pie chart for ax6
    data15 = fg_percent(last_5_games.at[4, 'FTM'], last_5_games.at[4, 'FTA'])
    ax18.pie(data15['sizes'], labels=data15['labels'], 
            colors=data15['colors'],
            wedgeprops=dict(width=0.3), 
            autopct='%1.1f%%',
            textprops={'size': 'medium', 'color': 'black'},
            startangle=60)
    ax18.set_title('',fontsize=17,fontweight='bold',color='black',pad=5)
    ax18.axis('equal')
    ######################################################################################


    # Adjust layout
    #plt.tight_layout()
    fig.tight_layout(pad=1)

    # Show the plot
    plt.show()

    #xx-small, x-small, small, medium, large, x-large, xx-large, larger, smaller


def last_10_games(player, nugget_regular_season_df):
    # Create a figure
    fig = plt.figure(figsize=(30, 15), facecolor='green')
    style.use('dark_background')

    # Set the spacing between subplots
    plt.subplots_adjust(hspace=0.3, wspace=0.2)  # Adjust the value as needed

    # Define the grid layout you want to use
    gs = gridspec.GridSpec(2, 5, width_ratios=[1, 1, 1, 1, 1])

    # Set the overall title for the figure
    fig.suptitle(f"{player}'s Scoring Pattern Distribution Over The Last 10 Games", color='orange', fontsize=30, fontweight='bold')

    # Prepare the data
    last_10_games = nugget_regular_season_df[nugget_regular_season_df.PLAYER == player ].tail(10).reset_index(drop=True)
    last_10_games["vs_opp_win_loss"] = last_10_games["Hm/Aw"]+' '+last_10_games["Opp"]+' '+last_10_games['W/L'] 
    last_10_games["GM_DAY"] = last_10_games["Date"]+' '+last_10_games["vs_opp_win_loss"]
    pie_df = last_10_games[["GM_DAY",'PLAYER',  '3PM', '2PM', 'FTM','Pts'  ]]
    #pie_df = pie_concat_df[['Country', '3PM', '2PM', 'FTM']].reset_index(drop=True)

    pie_df.loc[:, "3PM"] = pie_df["3PM"] * 3
    pie_df.loc[:, "2PM"] = pie_df["2PM"] * 2



    last_ten_games = pie_df

 
    # Create subplots
    axes = [
        fig.add_subplot(gs[0, 0], facecolor='black'),
        fig.add_subplot(gs[0, 1], facecolor='black'),
        fig.add_subplot(gs[0, 2], facecolor='black'),
        fig.add_subplot(gs[0, 3], facecolor='black'),
        fig.add_subplot(gs[0, 4], facecolor='black'),
        fig.add_subplot(gs[1, 0], facecolor='black'),
        fig.add_subplot(gs[1, 1], facecolor='black'),
        fig.add_subplot(gs[1, 2], facecolor='black'),
        fig.add_subplot(gs[1, 3], facecolor='black'),
        fig.add_subplot(gs[1, 4], facecolor='black')
    ]

    # Define colors
    cmaps = ["Reds", "Reds", "Blues", "Reds", "Reds", "Blues", "Blues", "Reds", "Blues", "Blues"]
    labels = ['3PM', '2PM', 'FTM']

    # Plot the pie charts
    for i, ax in enumerate(axes):
        if i < len(last_ten_games):
            piechart_df = last_ten_games.loc[i, labels]
            cmap = plt.get_cmap(cmaps[i % len(cmaps)])  
            colors = cmap([0.1, 0.5, 0.9])  
            
            ax.pie(piechart_df.values, autopct="%1.1f%%", labels=piechart_df.index, colors=colors, startangle=90, shadow=True, textprops={'size': 'xx-large', 'color': 'black'})
            ax.set_title(last_ten_games.loc[i, "GM_DAY"], fontweight='bold', fontsize=25, color='black')
            ax.axis('equal') 

    plt.show()


def stats_performance_trend(player, stat, label, color):
    style.use('dark_background')
    plt.figure(figsize=(30, 7))
    
    line_df = nugget_regular_season_df[nugget_regular_season_df.PLAYER == player ].tail(20)
    nugget_line_group_df = line_df.groupby(["GM_DAY"])[['MIN','Pts','FGA','FGM','3PM','3PA',  'FTM',
                                                                       'FTA', 'OREB','REB', 'AST', 'STL',
                                                                       'BLK', 'TOV', 'PF']].sum().astype(int)
  
    # Plot the lines for each stats
    plt.plot(nugget_line_group_df.index, nugget_line_group_df[stat],  label=label, marker='o', color=color)

    # Add data labels to each point
    for x, y in zip(nugget_line_group_df.index, nugget_line_group_df[stat]):
        plt.text(x, y, str(y), ha='center', va='bottom', fontsize=15, color='white')

    #for x, y in zip(nugget_line_group_df.index,  nugget_line_group_df['Opp_PTS']):
     #   plt.text(x, y, str(y), ha='center', va='bottom', fontsize=15, color='white')

    # Customize the plot
    plt.title(f" 2024-2025 NBA Regular Season  {player} {label} last 20 games.", fontsize=25, fontweight='bold', color='white')
    plt.xlabel('Date',color='orange',fontsize=25, fontweight='bold')
    plt.xticks( rotation=30,fontsize=20, fontweight='bold',color='yellowgreen')
    plt.yticks(fontsize=20, fontweight='bold',color='white')
   
    
    
    legend = plt.legend(fontsize=30)
    for text in legend.get_texts():
        text.set_color("orange")
        text.set_fontsize(20)
        text.set_fontweight('bold')


    # Show the plot
    plt.grid()
    plt.show()


len(nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin(["Nikola Jokic"])].reset_index(drop=True))


statistical_summary("PLAYER", "Nikola Jokic", "Nikola Jokic", "Greens")


performance_trend(nugget_regular_season_df, 'Nikola Jokic','Reds')


players_df = nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin(['Nikola Jokic'])]


nugget_regular_season_df.groupby(['PLAYER'])['AST'].mean()


annotation_text = (
   "This Joker is having another outstanding season in 2024-25!\n"
    "Here are some of his key stats so far:\n"
    "15,000 + career points in NBA\n"
    "- Average Points per Game (PTS): 31.5 (1st in the league)\n"
    "- Rebounds per Game (REB): 13.0 (3rd in the league)\n"
    "- Assists per Game (AST): 9.7 (2nd in the league)\n"
    "- Field Goal Percentage (FG%): 56.2\n"
    "- Three-Point Percentage (3P%): 49.6\n"
    "- Efficiency Per Game (EFF) 42.4 (1st in the league)\n"
    "- Triple-Doubles: 14 in 31 games (1st in the league)\n"
    "- Double-Doubles: 11 in 31 games\n"
    "Jokic continues to be a dominant force,\n"
    "and a turbo engine for the nuggets also leading the league\n"
    "in player efficiency rating.\n" 
    "He's also in the MVP race.\n "
    "It's impressive to see him maintain such high performance\n"
    "levels consistently. "
    "Do you think he'll win the MVP this season?"
)

season_average("Nikola Jokic", "nikola.jpg", annotation_text, "yellow", plt.get_cmap("Wistia"))


triple_double_df = tripple_double_double('Nikola Jokic')
triple_double_overview()

Total triple-double performances for Nikola Jokic: 14
Total double-double performances for Nikola Jokic: 11


total_stats = players_total_stats("PLAYER", "Nikola Jokic")
overall_stats("Nikola Jokic", 1500)


home_away_performances("Nikola Jokic")


fg_3pts_ft("Nikola Jokic")


last_10_games('Nikola Jokic', nugget_regular_season_df)


stats_performance_trend("Nikola Jokic", 'Pts', 'Points', 'green') 
stats_performance_trend("Nikola Jokic", 'REB', 'Rebounds', 'green') 
stats_performance_trend("Nikola Jokic", 'AST', 'Assists', 'green') 
stats_performance_trend("Nikola Jokic", 'BLK', 'Blocks', 'green') 
stats_performance_trend("Nikola Jokic", 'TOV', 'Turnovers', 'green')


performance_trend(nugget_regular_season_df, 'Aaron Gordon','viridis_r')


performance_trend(nugget_regular_season_df, 'Jamal Murray','viridis_r')


len(nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin(["Michael Porter"])].reset_index(drop=True))


performance_trend(nugget_regular_season_df, 'Michael Porter','Reds')


total_stats = players_total_stats("PLAYER", "Michael Porter")
overall_stats("Michael Porter",  1000)


nugget_regular_season_df.groupby(['PLAYER'])['EFF'].mean()


annotation_text = (
   "The One time NBA Champion Michael Porter in this season with the nuggets so far.\n"
    "Here are some of his average stats so far:\n"
    "- Average Points per Game (PTS): 19.1 \n"
    "- Rebounds per Game (REB): 6.5 \n"
    "- Assists per Game (AST): 2.6\n"
    "- Field Goal Percentage (FG%): 52.4\n"
    "- Three-Point Percentage (3P%): 43.3\n"
    "- Free Throw Percentage (FT%): 63.0\n"
    "- Efficiency Per Game (EFF) 20.6\n"
    "- Double-Doubles: 5\n"
    "Micheal Porter continues to be a contributing force,\n"
)

season_average("Michael Porter", "denver.jpg", annotation_text, "yellow", plt.get_cmap("winter"))


home_away_performances("Michael Porter")


tripple_double_double("Michael Porter")


fg_3pts_ft("Michael Porter")
last_10_games('Michael Porter', nugget_regular_season_df)


stats_performance_trend("Michael Porter", 'Pts', 'Points', 'green') 
stats_performance_trend("Michael Porter", 'REB', 'Rebounds', 'green') 
stats_performance_trend("Michael Porter", 'AST', 'Assists', 'green') 
stats_performance_trend("Michael Porter", 'BLK', 'Blocks', 'green')
stats_performance_trend("Michael Porter", 'TOV', 'Turnovers', 'green')


performance_trend(nugget_regular_season_df, 'Christian Braun','viridis_r')


len(nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin(["Russell Westbrook"])].reset_index(drop=True))


performance_trend(nugget_regular_season_df, 'Russell Westbrook','Reds')


total_stats = players_total_stats("PLAYER", "Russell Westbrook")
overall_stats("Russell Westbrook",  700)


annotation_text = (
   "This triple double king is having a good season with the nuggets so far.\n"
    "Starting off the the bench in most games,\n"
    "His playing styles correltes with the nugget's\n"
    "Here are some of his avearge stats so far:\n"
    "- Average Points per Game (PTS): 12.0 \n"
    "- Rebounds per Game (REB): 4.6 \n"
    "- Assists per Game (AST): 6.6\n"
    "- Field Goal Percentage (FG%): 45.4\n"
    "- Three-Point Percentage (3P%): 31.6\n"
    "- Free Throw Percentage (FT%): 63.0\n"
    "- Efficiency Per Game (EFF) 16.4\n"
    "- Triple-Doubles: 2\n"
    "- Double-Doubles: 4\n"
    "Russell Westbrook continues to be a contributing force,\n"
    "especially on defensive end for the nuggets.\n"
    "It's impressive to see him maintain such high performance\n"
    "levels consistently. "
 
)

season_average("Russell Westbrook", "denver.jpg", annotation_text, "yellow", plt.get_cmap("winter_r"))


nugget_regular_season_df.groupby(['PLAYER'])['EFF'].mean()


home_away_performances("Russell Westbrook")


tripple_double_double("Russell Westbrook")


fg_3pts_ft("Russell Westbrook")
last_10_games('Russell Westbrook', nugget_regular_season_df)


stats_performance_trend("Russell Westbrook", 'Pts', 'Points', 'green') 
stats_performance_trend("Russell Westbrook", 'REB', 'Rebounds', 'green') 
stats_performance_trend("Russell Westbrook", 'AST', 'Assists', 'green') 
stats_performance_trend("Russell Westbrook", 'BLK', 'Blocks', 'green')
stats_performance_trend("Russell Westbrook", 'TOV', 'Turnovers', 'green')


stats_performance_trend("Peyton Watson", 'MIN', 'Minutes', 'green') 
stats_performance_trend("Peyton Watson", 'Pts', 'Points', 'green') 
stats_performance_trend("Peyton Watson", 'REB', 'Rebounds', 'green') 
stats_performance_trend("Peyton Watson", 'AST', 'Assists', 'green') 
stats_performance_trend("Peyton Watson", 'BLK', 'Blocks', 'green') 
stats_performance_trend("Peyton Watson", 'TOV', 'Turnovers', 'green')


players_df = nugget_regular_season_df[nugget_regular_season_df['Date'].isin(['2025-01-09'])]


players_df = players_df.groupby(["PLAYER"])[['MIN','Pts','REB','AST', 'BLK',
                                                    'TOV', 'FGA', 'FGM',
                                                    '3PA', '3PM','2PM','2PA',"OREB","DREB", 
                                                    'FTA', 'FTM', 'PF','+/-','EFF']].sum().astype(int).sort_values(by='Pts',ascending=False)#.head(10)
players_df


style.use("dark_background")

# Calculate the percentage of each country
pct_df = players_df['Pts']
pct_df_pct = ((pct_df / pct_df.sum()) * 100).round(decimals=1)

# Create the bar plot
plt.figure(figsize=(5, 5))
bar_plot = sns.barplot(x=pct_df_pct, y=pct_df_pct.index, palette='viridis')
plt.ylabel(None)
plt.xlabel('Percentage')

# Add values on the bars
for index, value in enumerate(pct_df_pct):
    plt.text(value + 5, index, f'{value}%', va='center', ha='center', fontsize=12, fontweight='bold', color='orange')

plt.title('Percentage Distribution of Points vs Atlanta Hawks 2025-01-02', fontsize=15, fontweight='bold', color='white', pad=20)
plt.xticks(fontsize=14, color='yellowgreen')
plt.yticks(fontsize=14, color='yellowgreen')
plt.grid(False)

# Remove the rectangular borderlines
for spine in bar_plot.spines.values():
    spine.set_visible(False)

plt.tight_layout()
plt.show()

	MIN	Pts	REB	AST	BLK	TOV	FGA	FGM	3PA	3PM	2PM	2PA	OREB	DREB	FTA	FTM	PF	+/-	EFF
PLAYER
Jamal Murray	34	21	3	9	1	2	13	7	6	4	3	7	0	3	3	3	1	24	27
Michael Porter	27	19	8	2	0	2	12	8	5	3	5	7	0	8	0	0	1	15	24
Russell Westbrook	29	19	6	8	0	3	16	8	5	1	7	11	2	4	5	2	3	18	19
Julian Strawther	32	16	4	2	0	1	10	5	8	4	1	2	0	4	2	2	3	18	16
Christian Braun	28	15	2	1	0	0	8	6	2	2	4	6	0	2	1	1	4	11	17
DeAndre Jordan	23	12	9	2	0	0	6	5	0	0	5	6	2	7	3	2	2	17	23
Peyton Watson	24	9	4	1	2	1	10	4	2	0	4	8	0	4	3	1	3	17	8
Dario Saric	20	7	7	2	0	2	8	3	4	1	2	4	3	4	0	0	2	11	10
Jalen Pickett	4	6	0	0	0	0	2	2	2	2	0	0	0	0	0	0	0	-5	6
Zeke Nnaji	4	2	0	0	0	0	1	1	0	0	1	1	0	0	0	0	2	-5	2
Hunter Tyson	9	0	1	0	0	0	1	0	0	0	0	1	0	1	0	0	3	-1	0
Trey Alexander	4	0	1	2	0	0	2	0	1	0	0	1	0	1	0	0	0	-5	1

	Date	Hm/Aw	Opp	W/L	MIN	Pts	PLAYER	FGM	FGA	FG%	3PM	3PA	2PM	2PA	FTM	FTA	OREB	DREB	REB	AST	STL	BLK	TOV	PF	+/-	EFF
0	2024-10-25	vs	OKC	L	35	16	Nikola Jokic	6	13	46.2%	1	3	5	10	3	4	4	8	12	13	2	1	3	3	-9	33
1	2024-10-25	vs	OKC	L	29	16	Christian Braun	8	15	53.3%	0	3	8	12	0	0	2	5	7	1	2	2	3	1	8	18
2	2024-10-25	vs	OKC	L	32	15	Michael Porter	5	17	29.4%	3	10	2	7	2	2	1	7	8	2	1	0	1	2	-2	13
3	2024-10-25	vs	OKC	L	33	12	Aaron Gordon	5	12	41.7%	0	1	5	11	2	2	5	4	9	2	1	0	2	1	-4	15
4	2024-10-25	vs	OKC	L	38	12	Jamal Murray	4	13	30.8%	2	5	2	8	2	2	2	4	6	4	2	0	3	2	-2	12
5	2024-10-25	vs	OKC	L	21	6	Russell Westbrook	2	10	20.0%	1	6	1	4	1	4	1	4	5	5	1	2	2	0	-24	6
6	2024-10-25	vs	OKC	L	17	6	Julian Strawther	3	6	50.0%	0	2	3	4	0	0	0	0	0	1	1	0	1	2	-23	4
7	2024-10-25	vs	OKC	L	15	2	Peyton Watson	1	7	14.3%	0	4	1	3	0	0	2	4	6	1	0	2	0	0	-13	5
8	2024-10-25	vs	OKC	L	11	2	Dario Saric	1	2	50.0%	0	1	1	1	0	0	0	1	1	0	1	0	0	1	-6	3
9	2024-10-25	vs	OKC	L	2	0	Trey Alexander	0	0	-	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1
10	2024-10-25	vs	OKC	L	2	0	Vlatko Cancar	0	0	-	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1
11	2024-10-25	vs	OKC	L	2	0	Hunter Tyson	0	1	0.0%	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0
12	2024-10-25	vs	OKC	L	2	0	Jalen Pickett	0	1	0.0%	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	-1
13	2024-10-25	vs	OKC	L	2	0	Zeke Nnaji	0	1	0.0%	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	-1
14	2024-10-26	vs	LAC	L	37	41	Nikola Jokic	14	26	53.8%	7	12	7	14	6	8	3	6	9	4	2	1	5	2	7	38
15	2024-10-26	vs	LAC	L	37	22	Jamal Murray	7	14	50.0%	3	6	4	8	5	5	0	2	2	5	2	0	2	1	6	22
16	2024-10-26	vs	LAC	L	34	11	Christian Braun	4	6	66.7%	1	1	3	5	2	2	1	6	7	1	1	2	1	3	1	19
17	2024-10-26	vs	LAC	L	36	10	Aaron Gordon	3	10	30.0%	1	4	2	6	3	4	2	3	5	2	1	0	0	2	2	10
18	2024-10-26	vs	LAC	L	38	9	Michael Porter	4	13	30.8%	0	6	4	7	1	1	2	7	9	3	2	0	1	2	3	13
19	2024-10-26	vs	LAC	L	16	8	Julian Strawther	3	5	60.0%	2	2	1	3	0	0	1	1	2	3	2	0	1	5	-12	12
20	2024-10-26	vs	LAC	L	19	2	Russell Westbrook	0	8	0.0%	0	3	0	5	2	2	0	1	1	2	2	1	0	4	-13	0
21	2024-10-26	vs	LAC	L	12	1	Peyton Watson	0	3	0.0%	0	1	0	2	1	2	0	0	0	0	1	0	0	1	-7	-2
22	2024-10-26	vs	LAC	L	11	0	Dario Saric	0	2	0.0%	0	0	0	2	0	0	1	1	2	0	0	0	0	2	-12	0
23	2024-10-29	@	TOR	W	44	40	Nikola Jokic	18	27	66.7%	3	5	15	22	1	2	3	7	10	4	1	2	3	2	9	44
24	2024-10-29	@	TOR	W	40	17	Jamal Murray	6	20	30.0%	0	2	6	18	5	5	1	8	9	7	1	0	0	2	8	20
25	2024-10-29	@	TOR	W	40	17	Christian Braun	6	11	54.5%	1	4	5	7	4	4	2	2	4	2	1	1	1	3	8	19
26	2024-10-29	@	TOR	W	42	16	Aaron Gordon	4	8	50.0%	2	2	2	6	6	8	5	6	11	8	2	1	5	2	11	27
27	2024-10-29	@	TOR	W	39	13	Michael Porter	6	12	50.0%	1	4	5	8	0	1	2	7	9	2	0	1	1	3	-10	17
28	2024-10-29	@	TOR	W	18	9	Russell Westbrook	3	7	42.9%	0	1	3	6	3	4	1	3	4	3	1	0	2	4	-1	10
29	2024-10-29	@	TOR	W	22	9	Julian Strawther	3	3	100.0%	2	2	1	1	1	2	0	2	2	0	0	0	1	3	1	9
30	2024-10-29	@	TOR	W	15	6	Peyton Watson	1	5	20.0%	0	0	1	5	4	4	1	1	2	2	0	1	1	1	-7	6
31	2024-10-29	@	TOR	W	5	0	Dario Saric	0	3	0.0%	0	0	0	3	0	0	0	1	1	1	0	0	2	0	-9	-3
32	2024-10-30	@	BRK	W	41	29	Nikola Jokic	9	16	56.3%	0	0	9	16	11	13	6	12	18	16	0	1	1	3	8	54
33	2024-10-30	@	BRK	W	33	24	Aaron Gordon	8	11	72.7%	1	3	7	8	7	7	1	4	5	2	0	1	1	3	5	28
34	2024-10-30	@	BRK	W	37	24	Jamal Murray	8	19	42.1%	2	7	6	12	6	8	0	3	3	3	0	0	1	3	3	16
35	2024-10-30	@	BRK	W	21	22	Russell Westbrook	5	12	41.7%	2	2	3	10	10	10	0	1	1	5	1	0	1	2	0	21
36	2024-10-30	@	BRK	W	41	16	Michael Porter	6	11	54.5%	4	7	2	4	0	1	1	4	5	2	1	0	0	2	6	18
37	2024-10-30	@	BRK	W	39	12	Christian Braun	3	7	42.9%	2	3	1	4	4	6	0	3	3	3	0	2	0	4	9	14
38	2024-10-30	@	BRK	W	22	7	Julian Strawther	3	6	50.0%	1	3	2	3	0	0	0	4	4	0	1	0	1	0	-3	8
39	2024-10-30	@	BRK	W	20	7	Peyton Watson	3	5	60.0%	1	2	2	3	0	0	0	2	2	0	0	0	0	5	0	7

Denver Nuggets Players Analysis

Key Players and Their Contributions¶

Analytical Approach¶

Players Overall Summary Performance¶

Performance Trend¶

Game Summary¶

Season Average¶

Overall Stats¶

Tripple And Double Double Performances¶

Triple Double Overview¶

Home and Away Games Performance Insights:¶

Field goals, 3 Points, Free-throws attempts, made and percentages¶

Scoring Pattern Distribution¶

Performance( Points, Rebounds, Assists, Blocks .....) Trend¶

Nikola Jokic¶

Statitical Summary¶

Performance Trend¶

Performance Trends¶

Highest-scoring game:¶

Lowest-scoring games:¶

Rebounds¶

Season Average¶

Tripple Double And Double Double Performance Trend¶

Overall Performance¶

Home and Away Games Performance Insights:¶

Field goals, 3 Points, Free-throws attempts, made and percentages¶

Scoring Pattern Distribution¶

Analysis:¶

Recommendations:¶

Performance( Points, Rebounds, Assists, Blocks .....) Trend¶

Recommendations:¶

Inferences¶

Recommendations:¶

Aaron Gordon¶

Jamal Murray¶

Michael Porter¶

Overall Performance¶

Season Average¶

Home And Away Performance¶

Triple Double Double Double¶

Christian Braun¶

Russell Westbrook¶

Performance Trend¶

Overall Performance¶

Season Average¶

Home and Away Performance¶

Triple Double And Double Double Performance¶

Watson Payton¶

Point Contribution Per Game Distribution¶